Castorina Paolo, Ferini Gianluca, Martorana Emanuele, Forte Stefano
INFN, Sezione di Catania, 95123 Catania, Italy.
Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 18000 Prague, Czech Republic.
J Pers Med. 2022 Mar 26;12(4):530. doi: 10.3390/jpm12040530.
Tumor volume regression during and after chemo and radio therapy is a useful information for clinical decisions. Indeed, a quantitative, patient oriented, description of the response to treatment can guide towards the modification of the scheduled doses or the evaluation of the best time for surgery. We propose a macroscopic algorithm which permits to follow quantitatively the time evolution of the tumor volume during and after radiochemotherapy. The method, initially validated with different cell-lines implanted in mice, is then successfully applied to the available data for partially responding and complete recovery patients.
化疗和放疗期间及之后肿瘤体积的缩小是临床决策的有用信息。实际上,对治疗反应进行定量的、以患者为导向的描述可以指导调整预定剂量或评估最佳手术时间。我们提出了一种宏观算法,该算法能够定量跟踪放化疗期间及之后肿瘤体积的时间演变。该方法最初在植入小鼠体内的不同细胞系上得到验证,随后成功应用于部分缓解和完全康复患者的现有数据。